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We use Synthetic Control Methodology to estimate the output loss in Tunisia as a result of the “Arab Spring.” Our results suggest that the loss was 5.5 percent, 5.1 percent, and 6.4 percent of GDP in 2011, 2012, and 2013 respectively. These findings are robust to a series of tests, including placebo tests, and are consistent with those from an Autoregressive Distributed Lag Model of Tunisia’s economic growth. Moreover, we find that investment was the main channel through which the economy was adversely impacted by the Arab Spring.

The paper evaluates the Croatia’s seven-year membership in the European Union based on selected macroeconomic indicators by using a methodological approach, counterfactual analysis, and a synthetic control method. The results showed that the effect of the accession stimulated the economic growth and components of aggregate demand, income, savings and sectoral productivity. Also, strong disturbances with the onset of the crisis in 2009 were detected, the effects of which ultimately had a negative consequence in terms of more successful economic integration. Accession to the EU halted the decline in macroeconomic indicators and began a mild, but still insufficient recovery. The research confirms a strong trend of export development after 2013, a strong turn and increase in savings, a strong and significant decline in the value added of the agriculture sector as well as not recovered consumption. Also, the positive effect in the reduction of government expenditures is expressed.

This paper aims to shed some light on the issue of euro-induced inflation in the case of the Croatian euro changeover. Applying the synthetic control method, we were unable to find unambiguous and robust evidence of such an impact on the aggregate level. Focusing on a wide array of products and services, we found no impact of the euro on most price subcategories except those related to food, clothes and restaurant prices. The findings for the latter two categories seem particularly robust, surviving a battery of alternative specifications such as the generalized synthetic control and matrix completion method. Placebo tests reveal considerable ambiguity vis-à-vis the exact timing of the euro effect on prices, probably reflecting the fact that Croatia had been a highly euroized economy years before the de iure changeover.

International carbon markets are an appealing and increasingly popular tool to regulate carbon emissions. By putting a price on carbon, carbon markets reshape incentives faced by firms and reduce the value of emissions. How effective are carbon markets? Observers have tended to infer their effectiveness from market prices. The general belief is that a carbon market needs a high price in order to reduce emissions. As a result, many observers remain skeptical of initiatives such as the European Union Emissions Trading System (EU ETS), whose price remained low (compared to the social cost of carbon). In this paper, we assess whether the EU ETS reduced CO₂ emissions despite low prices. We motivate our study by documenting that a carbon market can be effective if it is a credible institution that can plausibly become more stringent in the future. In such a case, firms might cut emissions even though market prices are low. In fact, low prices can be a signal that the demand for carbon permits weakens. Thus, low prices are compatible with successful carbon markets. To assess whether the EU ETS reduced carbon emissions even as permits were cheap, we estimate counterfactual carbon emissions using an original sectoral emissions dataset. We find that the EU ETS saved about 1.2 billion tons of CO₂ between 2008 and 2016 (3.8%) relative to a world without carbon markets, or almost half of what EU governments promised to reduce under their Kyoto Protocol commitments. Emission reductions in sectors covered under the EU ETS were higher.

Reducing emissions from deforestation and forest degradation (REDD+) has gained international attention over the past decade, as manifested in both United Nations policy discussions and hundreds of voluntary projects launched to earn carbon-offset credits. There are ongoing discussions about whether and how projects should be integrated into national climate change mitigation efforts under the Paris Agreement. One consideration is whether these projects have generated additional impacts over and above national policies and other measures. To help inform these discussions, we compare the crediting baselines established ex-ante by voluntary REDD+ projects in the Brazilian Amazon to counterfactuals constructed ex-post based on the quasi-experimental synthetic control method. We find that the crediting baselines assume consistently higher deforestation than counterfactual forest loss in synthetic control sites. This gap is partially due to decreased deforestation in the Brazilian Amazon during the early implementation phase of the REDD+ projects considered here. This suggests that forest carbon finance must strike a balance between controlling conservation investment risk and ensuring the environmental integrity of carbon emission offsets. Relatedly, our results point to the need to better align project- and national-level carbon accounting.

Probably because of their interpretability and transparent nature, synthetic controls have become widely applied in empirical research in economics and the social sciences. This article aims to provide practical guidance to researchers employing synthetic control methods. The article starts with an overview and an introduction to synthetic control estimation. The main sections discuss the advantages of the synthetic control framework as a research design, and describe the settings where synthetic controls provide reliable estimates and those where they may fail. The article closes with a discussion of recent extensions, related methods, and avenues for future research.

We use the synthetic control method to analyze the effect of face masks on the spread of COVID-19 in Germany. Our identification approach exploits regional variation in the point in time when wearing of face masks became mandatory in public transport and shops. Depending on the region we consider, we find that face masks reduced the number of newly registered severe acute respiratory syndrome coronavirus 2 infections between 15% and 75% over a period of 20 days after their mandatory introduction. Assessing the credibility of the various estimates, we conclude that face masks reduce the daily growth rate of reported infections by around 47%.

This study numerically investigated the mechanisms of separation control using a synthetic jet (SJ) and plasma actuator (PA) around an NACA0015 airfoil at the chord Reynolds number of 63,000. Both SJ and PA were installed on the leading edge with the same order of input momentum (Cμ=O(10−3–10−5)) and the same actuation frequencies in F+=1.0–30. The momentum coefficient Cμ is defined as the normalized momentum introduced from the SJ or the PA, and F+ stands for the actuation frequency normalized by the chord length and uniform velocity. A number of large-eddy simulations (LES) were conducted for the SJ and the PA, and the mechanisms were clarified in terms of the exchange of chordwise momentum with Reynolds shear stress and coherent vortex structures. First, four main differences in the induced flows of the SJ and the PA were clarified as follows: (A) wall-tangential velocity; (B) three-dimensional flow structures; (C) spatial locality; and (D) temporal fluctuation. Then, a common feature of flow control by the SJ and the PA was revealed: a lift-to-drag ratio was found to be better recovered in F+=6.0–20 than in other frequencies. Although there were differences in the induced flows, the phase decomposition of the flow fields identified common mechanisms that the turbulent component of the Reynolds shear stress mainly contributes to the exchange of the chordwise (streamwise) momentum; and the turbulent vortices are convected over the airfoil surface by the coherent spanwise vortices in the frequency of F+.

An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response in a synthetic control that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including local trends, seasonality and the time-varying influence of contemporaneous covariates. Using a Markov chain Monte Carlo algorithm for posterior inference, we illustrate the statistical properties of our approach on simulated data. We then demonstrate its practical utility by estimating the causal effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of state-space models in enabling causal attribution in those settings where a randomised experiment is unavailable. The CausalImpact R package provides an implementation of our approach.

We analyze the properties of the Synthetic Control (SC) and related estimators when the pre-treatment fit is imperfect. In this framework, we show that these estimators are generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre-treatment periods goes to infinity. Still, we show that a demeaned version of the SC method can improve in terms of bias and variance relative to the difference-in-difference estimator. We also derive a specification test for the demeaned SC estimator in this setting with imperfect pre-treatment fit. Given our theoretical results, we provide practical guidance for applied researchers on how to justify the use of such estimators in empirical applications.